Method and system for acquiring solid phase diffusion coefficient and exchange current density

Abstract

The embodiment of the invention provides a solid phase diffusion coefficient and exchange current density acquisition method and system, and the method comprises the steps: carrying out the low-rate charge and discharge test of a target battery, recording the open-circuit voltage and the charge and discharge specific capacity of the charge and discharge test, determining an SOC-OCV curve based on the open-circuit voltage, and determining an SOL-OCV curve in combination with the charge and discharge specific capacity; performing half-cell testing on the target battery to obtain test data, acquiring OCV data and actually measured DCR data of the target battery under different multiplying powers, establishing a battery behavior model based on the test data and the OCV data, and performing reverse optimization on an SOL-OCV curve in combination with an optimization algorithm; and taking the SOL-OCV curve as simulation input of the electrochemical model, adjusting the solid-phase diffusion coefficient and the exchange current density of the electrochemical model, enabling a difference value between output simulation DCR data and actually measured DCR data to serve as a loss function, carrying out iterative training on the electrochemical model, and outputting the solid-phase diffusion coefficient and the exchange current density.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium batteries, and in particular to a method and system for acquiring a solid-phase diffusion coefficient and an exchange current density. Background Art

[0002] With the rapid development of new energy, lithium-ion batteries are widely used in various industries due to their environmental protection, good cycle performance and high energy density. For example, they are used in 3C products, electric vehicles and various facility energy storage devices. During use, the performance of lithium-ion batteries has received extensive attention from battery manufacturers and users.

[0003] At present, the performance of lithium-ion batteries is mainly predicted by electrochemical simulation. However, based on the electrochemical principle, many electrochemical parameters need to be tested, among which the solid phase diffusion coefficient and exchange current density are particularly important. The solid phase diffusion coefficient is mainly tested by EIS, GITT, and PITT, and the exchange current density is generally tested by LSV and CV. However, these test methods generally have certain limitations and are very time-consuming. Summary of the invention

[0004] In view of the problems existing in the prior art, an embodiment of the present invention provides a method and system for obtaining solid-phase diffusion coefficient and exchange current density.

[0005] An embodiment of the present invention provides a method for obtaining a solid phase diffusion coefficient and an exchange current density, the method comprising:

[0006] Performing a low-rate charge and discharge test on the target battery, recording the open circuit voltage and charge and discharge specific capacity of the charge and discharge test, determining a SOC-OCV curve based on the open circuit voltage, and determining a SOL-OCV curve in combination with the charge and discharge specific capacity;

[0007] Perform a half-cell test on the target battery to obtain test data, obtain OCV data and measured DCR data of the target battery at different rates, establish a battery behavior model based on the test data and OCV data, and reversely optimize the SOL-OCV curve in combination with an optimization algorithm;

[0008] The SOL-OCV curve is used as the simulation input of the electrochemical model, the solid phase diffusion coefficient and the exchange current density of the electrochemical model are adjusted, the difference between the output simulated DCR data and the measured DCR data is used as the loss function, the electrochemical model is iteratively trained, and the solid phase diffusion coefficient and the exchange current density are output.

[0009] In one embodiment, the method further comprises:

[0010] Perform LFP half-cell and graphite half-cell tests on the target battery, set the test rate and charge and discharge mode, and obtain the charge and discharge cut-off voltage of the half-cell test.

[0011] In one embodiment, the method further comprises:

[0012] Based on the charge and discharge cut-off voltages in the LFP half-cell and the graphite half-cell during the charge and discharge process, the ratio of the accumulated power to the theoretical capacity is calculated, and the SOC value corresponding to each OCV point is further calculated;

[0013] The SOC value of the LFP half-cell is directly replaced by the SOL value, and the graphite half-cell is converted to the SOL value according to the fully lithiated state or the fully delithiated state.

[0014] In one embodiment, the method further comprises:

[0015] The initial value and maximum value of the SOL-OCV curve are set based on the test data, and the SOL-OCV curve is adjusted by a numerical optimization algorithm, so that the function comparing the simulation output of the battery behavior model with the OCV data at different rates converges, and the converged SOL-OCV curve is output.

[0016] In one embodiment, the method further comprises:

[0017] The electrochemical model is tested based on the solid-phase diffusion coefficient and the exchange current density to determine the data sensitivity of the electrochemical model to the solid-phase diffusion coefficient and the exchange current density.

[0018] An embodiment of the present invention provides a system for acquiring a solid phase diffusion coefficient and an exchange current density, the system comprising:

[0019] A test module, used to perform a low-rate charge and discharge test on a target battery, record an open circuit voltage and a charge and discharge specific capacity of the charge and discharge test, determine a SOC-OCV curve based on the open circuit voltage, and determine a SOL-OCV curve in combination with the charge and discharge specific capacity;

[0020] A reverse optimization module is used to perform a half-cell test on the target battery to obtain test data, obtain OCV data and measured DCR data of the target battery at different rates, establish a battery behavior model based on the test data and OCV data, and perform reverse optimization on the SOL-OCV curve in combination with an optimization algorithm;

[0021] The output module is used to use the SOL-OCV curve as a simulation input of the electrochemical model, adjust the solid phase diffusion coefficient and the exchange current density of the electrochemical model, use the difference between the output simulated DCR data and the measured DCR data as a loss function, iteratively train the electrochemical model, and output the solid phase diffusion coefficient and the exchange current density.

[0022] In one embodiment, the system further comprises:

[0023] The half-cell test module is used to perform LFP half-cell and graphite half-cell tests on the target battery, set the test rate and charge and discharge mode, and obtain the charge and discharge cut-off voltage of the half-cell test.

[0024] In one embodiment, the system further comprises:

[0025] A calculation module is used to calculate the ratio of the accumulated power to the theoretical capacity based on the charge and discharge cut-off voltages in the LFP half-cell and the graphite half-cell during the charge and discharge process, and further calculate the SOC value corresponding to each OCV point;

[0026] The conversion module is used to directly replace the SOC value of the LFP half-cell with the SOL value, and convert the graphite half-cell into the SOL value according to the fully lithiated state or the fully delithiated state.

[0027] An embodiment of the present invention provides an electronic device, including a processor and a memory;

[0028] The processor is connected to the memory;

[0029] The memory is used to store executable program code;

[0030] The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method described in one or more embodiments.

[0031] An embodiment of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for obtaining the solid-phase diffusion coefficient and the exchange current density are implemented.

[0032] In view of the above, in one or more embodiments of the present specification, a small rate charge and discharge test is performed on the target battery, the open circuit voltage and charge and discharge specific capacity of the charge and discharge test are recorded, the SOC-OCV curve is determined based on the open circuit voltage, and the SOL-OCV curve is determined in combination with the charge and discharge specific capacity; a half-battery test is performed on the target battery to obtain test data, the OCV data and measured DCR data of the target battery at different rates are obtained, and a battery behavior model is established based on the test data and the OCV data, and the SOL-OCV curve is reversely optimized in combination with the optimization algorithm; the SOL-OCV curve is used as the simulation input of the electrochemical model, the solid phase diffusion coefficient and exchange current density of the electrochemical model are adjusted, and the difference between the output simulated DCR data and the measured DCR data is used as the loss function, and the electrochemical model is iteratively trained to output the solid phase diffusion coefficient and exchange current density. In this way, the solid phase diffusion coefficient and exchange current density can be determined on the premise of only obtaining OCV test data and DCR measured data, which saves time and testing costs compared with other complex electrochemical parameter testing methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 This is a flow chart of a method for obtaining solid-phase diffusion coefficient and exchange current density provided in one embodiment of this specification.

[0035] Figure 2 It is a structural schematic diagram of a system for acquiring solid-phase diffusion coefficient and exchange current density provided in one embodiment of this specification.

[0036] Figure 3 It is a structural schematic diagram of an electronic device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0037] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and is not a limitation of the scope of protection, applicability or examples set forth in the claims. The functions and arrangements of the elements discussed can be changed without departing from the scope of protection of the contents of this specification. Various examples can omit, replace or add various processes or components as needed. For example, the described method can be performed in an order different from the described order, and various steps can be added, omitted or combined. In addition, the features described relative to some examples can also be combined in other examples.

[0038] As used herein, the term "including" and its variations represent open terms, meaning "including but not limited to". The term "based on" means "based at least in part on". The terms "one embodiment" and "an embodiment" mean "at least one embodiment". The term "another embodiment" means "at least one other embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other definitions may be included below, whether explicit or implicit. Unless the context clearly indicates otherwise, the definition of a term is consistent throughout the specification.

[0039] like Figure 1 As shown, an embodiment of the present invention provides a method for obtaining a solid phase diffusion coefficient and an exchange current density, comprising:

[0040] Step S101, performing a small rate charge and discharge test on a target battery, recording an open circuit voltage and a charge and discharge specific capacity of the charge and discharge test, determining a SOC-OCV curve based on the open circuit voltage, and determining a SOL-OCV curve in combination with the charge and discharge specific capacity.

[0041] Specifically, for the target battery that needs to obtain the key parameters of lithium ions, the solid phase diffusion coefficient and the exchange current density, a small rate charge and discharge test is performed on the target battery, and the open circuit voltage and charge and discharge specific capacity of the charge and discharge test are recorded. The charge and discharge process at a small rate is slower, which helps to reduce the influence of the voltage drop caused by the internal resistance, thereby more truly reflecting the change of the open circuit voltage. According to the recorded open circuit voltage, the SOC-OCV curve can be established, where SOC (State of Charge): represents the ratio of the current remaining power of the battery to the full power, and OCV (Open Circuit Voltage): represents the battery voltage measured without an external load. The charge and discharge specific capacity refers to the amount of power that can be provided by a unit mass of active material. When generating a SOL-OCV curve (SOL (State of Lithiation): describes the degree of embedding of lithium ions in the positive or negative electrode material), the specific SOL-OCV curve generation process can convert the SOC-OCV curve into a SOL-OCV curve through the charge and discharge specific capacity.

[0042] Step S102, performing a half-battery test on the target battery to obtain test data, obtaining OCV data and measured DCR data of the target battery at different rates, establishing a battery behavior model based on the test data and OCV data, and combining with an optimization algorithm to reversely optimize the SOL-OCV curve.

[0043] Specifically, a half-cell test is performed on the target battery, wherein the half-cell test is to assemble the positive electrode or negative electrode material with lithium metal as the counter electrode into a battery for testing, obtain the half-cell test data, and obtain the OCV data of the target battery at different rates, that is, the OCV data of the complete battery composed of the positive electrode, negative electrode and electrolyte used in the actual measurement at different charge and discharge rates, so as to determine the performance of the battery under the actual working conditions. And obtain the measured DCR data of the battery, DCR (Direct Current Internal Resistance) represents the internal resistance of the battery under direct current conditions, the OCV data at different charge and discharge rates and the measured DCR data are the two data required in this embodiment, wherein the OCV data at different charge and discharge rates are used to cooperate with the test data to establish a battery behavior model, and the measured DCR data are used to verify the internal resistance characteristics in the simulation model in the subsequent steps. After the battery behavior model is established, the SOL-OCV curve is reversely optimized in combination with the optimization algorithm. Among them, the reverse optimization process can be: parameter initialization: based on the half-cell test results, set the initial value and maximum value of the SOL-OCV curve; data fitting: use numerical optimization algorithms (such as least squares, genetic algorithms, particle swarm optimization, etc.) to adjust the parameters of the SOL-OCV curve so that the OCV output by the simulation is as close as possible to the measured OCV of the full battery at different rates. Among them, data fitting includes defining the objective function (usually the sum of squared errors between the simulated OCV and the measured OCV) and iterative optimization steps (by continuously adjusting the parameters of the SOL-OCV curve to minimize the objective function. After each iteration, calculate the new simulated OCV and compare it with the measured OCV until the convergence condition is met).

[0044] Step S103, using the SOL-OCV curve as a simulation input of the electrochemical model, adjusting the solid phase diffusion coefficient and the exchange current density of the electrochemical model, using the difference between the output simulated DCR data and the measured DCR data as a loss function, iteratively training the electrochemical model, and outputting the solid phase diffusion coefficient and the exchange current density.

[0045] Specifically, the electrochemical model includes the solid phase diffusion coefficient (D s) and exchange current density (i0), the model should be able to simulate the voltage response, capacity change and internal resistance characteristics of the battery under different working conditions. Set the initial values ​​of the solid phase diffusion coefficient and the exchange current density, and then use the difference between the output simulation DCR data and the measured DCR data as the loss function to iteratively train the electrochemical model, where the initial value can be set manually by the staff or determined by historical simulation records. The process of iterative training of the electrochemical model can, for example, use a numerical optimization algorithm to make the DCR output by the simulation as consistent as possible with the measured DCR, and output the solid phase diffusion coefficient and exchange current density after iterative training.

[0046] In addition, the obtained solid-phase diffusion coefficient and exchange current density can be used to perform reverse model detection on the electrochemical model, and the data sensitivity of the electrochemical model to the solid-phase diffusion coefficient and exchange current density can be determined through the data gap in the model.

[0047] For the above steps, this implementation provides a specific solution for converting the SOC-OCV curve into the SOL-OCV curve and reverse optimization. In this solution, there is no obvious order for performing low-rate charge and discharge tests and half-cell tests on the target battery. The subsequent SOL conversion can be performed through the charge and discharge cut-off voltage of the half-cell test, including:

[0048] 1. Determine the test equipment and materials: half-cell configuration (positive half-cell: lithium iron phosphate (LFP) / lithium metal, negative half-cell: graphite / lithium metal), electrolyte (select electrolyte suitable for LFP and graphite electrodes), diaphragm (select appropriate diaphragm to prevent short circuit).

[0049] 2. Determine the test parameters: rate: 0.05C (i.e. the charging or discharging current is 5% of the battery capacity), charge and discharge mode: constant current / constant voltage (CC / CV), that is, after charging to the set voltage, switch to constant voltage mode until the current drops to a certain threshold (e.g. C / 20), and then discharge to the set cut-off voltage.

[0050] 3. Determine the charge and discharge cut-off voltages: LFP half-cell (charge cut-off voltage: 3.75V, discharge cut-off voltage: 2V), graphite half-cell (charge cut-off voltage: 2V, discharge cut-off voltage: 0.005V).

[0051] 4. Loop processing: Loop three times to ensure data stability and repeatability.

[0052] 5. Data collection: Record the open circuit voltage (OCV) at different SOC states in each cycle, and record the specific capacity during each charge and discharge process for subsequent conversion to SOL-OCV data.

[0053] 6. Data processing:

[0054] Calculate SOC: Calculate the SOC value corresponding to each OCV point based on the ratio of the accumulated power during the charging and discharging process to the theoretical capacity;

[0055] Convert SOL: Convert SOC-OCV data to SOL-OCV data, including: For LFP half-cells, SOC can be directly regarded as SOL, because the amount of lithium ions embedded in LFP is proportional to SOC; For graphite half-cells, it is necessary to consider the embedding mechanism of lithium ions in graphite, and SOL can be inferred from the known graphite lithium embedding chemical formula LiC6. For example, when x=1, it means that a carbon atom is embedded with a lithium ion, corresponding to a fully lithiated state; when x=0, it means that there is no lithium ion embedded, corresponding to a fully delithiated state.

[0056] 7. Obtain SOL-OCV curve

[0057] The OCV value corresponding to each SOC point is converted into the corresponding SOL value, and then the SOL-OCV curve is drawn. The SOL-OCV curve describes the distribution of lithium ions in the electrode material and its corresponding open circuit voltage.

[0058] The calculation formula for SOL-OCV data includes:

[0059] positive electrode:

[0060] SOLmax_=Q 3-ch / Q 理论

[0061] SOLmin=(Q 理论 -Q 3-ch ) / Q 理论

[0062] negative electrode:

[0063] SOLmax=Q 3-dis / Q 理论

[0064] SOLmin=(Q 理论 -Q 3-dis ) / Q 理论

[0065] Q theory is the theoretical gram capacity of graphite / LFP, Q1-ch is the specific capacity of the first charge cycle, and Q3-dis is the specific capacity of the third discharge cycle. The above formula is used to obtain the initial SOL values ​​of the positive and negative electrodes. However, due to the differences between half-powered and full batteries, this method will still have certain errors. It is necessary to use the OCV of the third cycle of the positive and negative electrodes with a 0.05C charge and the OCV of the soft-pack battery at 0.5C and 1.5C to use software for reverse optimization. The maximum and minimum values ​​of the optimized positive and negative electrode SOL are as follows:

[0066]

[0067]

[0068] Table 1

[0069] According to the battery design parameters and the electrochemical parameters in the database, a DCR model is established for simulation, and the battery electrochemical parameters are reversely optimized. The DCR operating condition is a 10s pulse discharge with a rate of 1C.

[0070] According to the measured data, the measured data of 1C discharge is:

[0071]

[0072] Taking the average value, the DCR at 20%, 50% and 90% SOC is 0.51, 0.46, 0.44 respectively, and the electrochemical parameter diffusion coefficient is reversely optimized according to these three values ​​as follows:

[0073]

[0074] A method for obtaining a solid-phase diffusion coefficient and an exchange current density provided by an embodiment of the present invention is to perform a small-rate charge-discharge test on a target battery, record the open circuit voltage and charge-discharge specific capacity of the charge-discharge test, determine the SOC-OCV curve based on the open circuit voltage, and determine the SOL-OCV curve in combination with the charge-discharge specific capacity; perform a half-battery test on the target battery to obtain test data, obtain the OCV data and measured DCR data of the target battery at different rates, establish a battery behavior model based on the test data and the OCV data, and reversely optimize the SOL-OCV curve in combination with the optimization algorithm; use the SOL-OCV curve as the simulation input of the electrochemical model, adjust the solid-phase diffusion coefficient and the exchange current density of the electrochemical model, use the difference between the output simulated DCR data and the measured DCR data as the loss function, iteratively train the electrochemical model, and output the solid-phase diffusion coefficient and the exchange current density. In this way, the solid-phase diffusion coefficient and the exchange current density can be determined on the premise that only the OCV test data and the measured DCR data need to be obtained, which saves time and testing costs compared with other complex electrochemical parameter testing methods.

[0075] See also Figure 2 , Figure 2 Schematic diagram of a system for obtaining solid phase diffusion coefficient and exchange current density provided in an embodiment of the present application. Figure 2 As shown, the system comprises:

[0076] The test module S201 is used to perform a low-rate charge and discharge test on the target battery, record the open circuit voltage and charge and discharge specific capacity of the charge and discharge test, determine the SOC-OCV curve based on the open circuit voltage, and determine the SOL-OCV curve in combination with the charge and discharge specific capacity;

[0077] A reverse optimization module S202 is used to perform a half-cell test on the target battery to obtain test data, obtain OCV data and measured DCR data of the target battery at different rates, establish a battery behavior model based on the test data and OCV data, and perform reverse optimization on the SOL-OCV curve in combination with an optimization algorithm;

[0078] The output module S203 is used to use the SOL-OCV curve as the simulation input of the electrochemical model, adjust the solid phase diffusion coefficient and the exchange current density of the electrochemical model, use the difference between the output simulated DCR data and the measured DCR data as the loss function, iteratively train the electrochemical model, and output the solid phase diffusion coefficient and the exchange current density.

[0079] In another embodiment, a system for acquiring solid phase diffusion coefficient and exchange current density further includes:

[0080] The half-cell test module is used to perform LFP half-cell and graphite half-cell tests on the target battery, set the test rate and charge and discharge mode, and obtain the charge and discharge cut-off voltage of the half-cell test.

[0081] In another embodiment, a system for acquiring solid phase diffusion coefficient and exchange current density further includes:

[0082] A calculation module is used to calculate the ratio of the accumulated power to the theoretical capacity based on the charge and discharge cut-off voltages in the LFP half-cell and the graphite half-cell during the charge and discharge process, and further calculate the SOC value corresponding to each OCV point;

[0083] The conversion module is used to directly replace the SOC value of the LFP half-cell with the SOL value, and convert the graphite half-cell into the SOL value according to the fully lithiated state or the fully delithiated state.

[0084] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (Field-Programmable Gate Array, FPGA), an integrated circuit (Integrated Circuit, IC), etc.

[0085] Each processing unit and / or module of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.

[0086] See also Figure 3 , which shows a schematic diagram of the structure of an electronic device involved in an embodiment of the present application, the electronic device can be used to implement Figure 1 The method in the embodiment shown. Figure 3 As shown, the electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0087] The communication bus 302 is used to realize the connection and communication between these components.

[0088] The user interface 303 may include a display screen (Display) and a camera (Camera), and the optional user interface 303 may also include a standard wired interface and a wireless interface.

[0089] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0090] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts of the entire electronic device 300, and executes various functions and processes data of the terminal 300 by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or more combinations of processors (Central Processing Unit, CPU), image processors (Graphics Processing Unit, GPU) and modems. Among them, the CPU mainly processes operating systems, user interfaces, and applications; the GPU is responsible for rendering and drawing the content that needs to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0091] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0092] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the interactive application based on image generation stored in the memory 305, and specifically perform the following operations: perform a small rate charge and discharge test on the target battery, record the open circuit voltage and charge and discharge specific capacity of the charge and discharge test, determine the SOC-OCV curve based on the open circuit voltage, and determine the SOL-OCV curve in combination with the charge and discharge specific capacity; perform a half-battery test on the target battery to obtain test data, obtain the OCV data and measured DCR data of the target battery at different rates, establish a battery behavior model based on the test data and the OCV data, and reversely optimize the SOL-OCV curve in combination with the optimization algorithm; use the SOL-OCV curve as the simulation input of the electrochemical model, adjust the solid phase diffusion coefficient and exchange current density of the electrochemical model, use the difference between the output simulated DCR data and the measured DCR data as the loss function, iteratively train the electrochemical model, and output the solid phase diffusion coefficient and exchange current density.

[0093] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0094] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0095] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0096] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0097] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual measurement needs to achieve the purpose of the solution of this embodiment.

[0098] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0099] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program code.

[0100] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by entering a program to instruct the relevant hardware. The program may be stored in a computer-readable memory, and the memory may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0101] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for obtaining a solid phase diffusion coefficient and an exchange current density, the method comprising: Performing a low-rate charge and discharge test on the target battery, recording the open circuit voltage and charge and discharge specific capacity of the charge and discharge test, determining a SOC-OCV curve based on the open circuit voltage, and determining a SOL-OCV curve in combination with the charge and discharge specific capacity; Perform a half-cell test on the target battery to obtain test data, obtain OCV data and measured DCR data of the target battery at different rates, establish a battery behavior model based on the test data and OCV data, and reversely optimize the SOL-OCV curve in combination with an optimization algorithm; The SOL-OCV curve is used as the simulation input of the electrochemical model, the solid phase diffusion coefficient and the exchange current density of the electrochemical model are adjusted, the difference between the output simulated DCR data and the measured DCR data is used as the loss function, the electrochemical model is iteratively trained, and the solid phase diffusion coefficient and the exchange current density are output.

2. The method according to claim 1, characterized in that The method further comprises: Perform LFP half-cell and graphite half-cell tests on the target battery, set the test rate and charge and discharge mode, and obtain the charge and discharge cut-off voltage of the half-cell test.

3. The method according to claim 2, characterized in that The method further comprises: Based on the charge and discharge cut-off voltages in the LFP half-cell and the graphite half-cell during the charge and discharge process, the ratio of the accumulated power to the theoretical capacity is calculated, and the SOC value corresponding to each OCV point is further calculated; The SOC value of the LFP half-cell is directly replaced by the SOL value, and the graphite half-cell is converted to the SOL value according to the fully lithiated state or the fully delithiated state.

4. The method according to claim 1, characterized in that: The reverse optimization of the SOL-OCV curve comprises: The initial value and maximum value of the SOL-OCV curve are set based on the test data, and the SOL-OCV curve is adjusted by a numerical optimization algorithm, so that the function comparing the simulation output of the battery behavior model with the OCV data at different rates converges, and the converged SOL-OCV curve is output.

5. The method according to claim 1, characterized in that The method further comprises: The electrochemical model is tested based on the solid-phase diffusion coefficient and the exchange current density to determine the data sensitivity of the electrochemical model to the solid-phase diffusion coefficient and the exchange current density.

6. A system for acquiring solid phase diffusion coefficient and exchange current density, characterized in that: The system comprises: A test module, used to perform a low-rate charge and discharge test on a target battery, record an open circuit voltage and a charge and discharge specific capacity of the charge and discharge test, determine a SOC-OCV curve based on the open circuit voltage, and determine a SOL-OCV curve in combination with the charge and discharge specific capacity; A reverse optimization module is used to perform a half-cell test on the target battery to obtain test data, obtain OCV data and measured DCR data of the target battery at different rates, establish a battery behavior model based on the test data and OCV data, and perform reverse optimization on the SOL-OCV curve in combination with an optimization algorithm; The output module is used to use the SOL-OCV curve as a simulation input of the electrochemical model, adjust the solid phase diffusion coefficient and the exchange current density of the electrochemical model, use the difference between the output simulated DCR data and the measured DCR data as a loss function, iteratively train the electrochemical model, and output the solid phase diffusion coefficient and the exchange current density.

7. The system according to claim 6, characterized in that The system further comprises: The half-cell test module is used to perform LFP half-cell and graphite half-cell tests on the target battery, set the test rate and charge and discharge mode, and obtain the charge and discharge cut-off voltage of the half-cell test.

8. The system according to claim 7, characterized in that The system further comprises: A calculation module is used to calculate the ratio of the accumulated power to the theoretical capacity based on the charge and discharge cut-off voltages in the LFP half-cell and the graphite half-cell during the charge and discharge process, and further calculate the SOC value corresponding to each OCV point; The conversion module is used to directly replace the SOC value of the LFP half-cell with the SOL value, and convert the graphite half-cell into the SOL value according to the fully lithiated state or the fully delithiated state.

9. An electronic device comprising a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method according to any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method according to any one of claims 1 to 5.

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